Vision on Wheels: Looking at Driver, Vehicle, and Surround for On-Road Maneuver Analysis

Eshed Ohn-Bar, Ashish Tawari, Sujitha Martin, Mohan M. Trivedi
2014 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops  
Automotive systems provide a unique opportunity for mobile vision technologies to improve road safety by understanding and monitoring the driver. In this work, we propose a real-time framework for early detection of driver maneuvers. The implications of this study would allow for better behavior prediction, and therefore the development of more efficient advanced driver assistance and warning systems. Cues are extracted from an array of sensors observing the driver (head, hand, and foot), the
more » ... vironment (lane and surrounding vehicles), and the ego-vehicle state (speed, steering angle, etc.). Evaluation is performed on a real-world dataset with overtaking maneuvers, showing promising results. In order to gain better insight into the processes that characterize driver behavior, temporally discriminative cues are studied and visualized.
doi:10.1109/cvprw.2014.33 dblp:conf/cvpr/Ohn-BarTMT14 fatcat:yxvxxehfd5gz7bd2kewwpcvpca